SOTAVerified

Emotion Recognition

Emotion Recognition is an important area of research to enable effective human-computer interaction. Human emotions can be detected using speech signal, facial expressions, body language, and electroencephalography (EEG). Source: Using Deep Autoencoders for Facial Expression Recognition

Papers

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Showing 1101–1125 of 2041 papers

TitleStatusHype
Privacy-Preserving Video Classification with Convolutional Neural Networks—0
Private Speech Classification with Secure Multiparty Computation—0
Probing Speech Emotion Recognition Transformers for Linguistic Knowledge—0
Progressive Graph Convolution Network for EEG Emotion Recognition—0
Progressive Modality Reinforcement for Human Multimodal Emotion Recognition From Unaligned Multimodal Sequences—0
Progressive Residual Extraction based Pre-training for Speech Representation Learning—0
Prompting Audios Using Acoustic Properties For Emotion Representation—0
Prosodic Structure Beyond Lexical Content: A Study of Self-Supervised Learning—0
PSO Fuzzy XGBoost Classifier Boosted with Neural Gas Features on EEG Signals in Emotion Recognition—0
PsyCounAssist: A Full-Cycle AI-Powered Psychological Counseling Assistant System—0
psyML at SemEval-2018 Task 1: Transfer Learning for Sentiment and Emotion Analysis—0
Push the Limit of Multi-modal Emotion Recognition by Prompting LLMs with Receptive-Field-Aware Attention Weighting—0
Qieemo: Speech Is All You Need in the Emotion Recognition in Conversations—0
Quality at the Tail of Machine Learning Inference—0
Reading Smiles: Proxy Bias in Foundation Models for Facial Emotion Recognition—0
Real-time Automatic Emotion Recognition from Body Gestures—0
Real-time EEG-based Emotion Recognition using Discrete Wavelet Transforms on Full and Reduced Channel Signals—0
Real Time Emotion Analysis Using Deep Learning for Education, Entertainment, and Beyond—0
Real-Time Imitation of Human Head Motions, Blinks and Emotions by Nao Robot: A Closed-Loop Approach—0
Real-Time Speech Emotion and Sentiment Recognition for Interactive Dialogue Systems—0
Real-time Speech Emotion Recognition Based on Syllable-Level Feature Extraction—0
Recognition of Emotions using Kinects—0
Recognizing Emotion Regulation Strategies from Human Behavior with Large Language Models—0
Recognizing Emotions in Video Using Multimodal DNN Feature Fusion—0
Recognizing More Emotions with Less Data Using Self-supervised Transfer Learning—0
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All datasetsEmomusicRAVDESSEMOTICFER2013MaSaC_ERCMPEDMSP-PodcastSEEDคลิปคุณพ่อให้ลูกสาวยืมโทรศัพท์และความสนุกสนาน

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1M2D-CLAPEmoA77.4—Unverified
2M2D2EmoA76.7—Unverified
3M2DEmoA76.1—Unverified
4Jukebox (Pre-training: CALM)EmoA72.1—Unverified
5CLMR (Pre-training: contrastive)EmoA67.8—Unverified
#ModelMetricClaimedVerifiedStatus
1LogisticRegression on posteriors of xlsr-Wav2Vec2.0&bi-LSTM+AttentionAccuracy86.7—Unverified
2MultiMAE-DERWAR83.61—Unverified
3Intermediate-Attention-FusionAccuracy81.58—Unverified
4Logistic Regression on posteriors of the CNN-14&biLSTM-GuidedSTAccuracy80.08—Unverified
5ERANN-0-4Accuracy74.8—Unverified
#ModelMetricClaimedVerifiedStatus
1CAGETop-3 Accuracy (%)14.73—Unverified
2FocusCLIPTop-3 Accuracy (%)13.73—Unverified
#ModelMetricClaimedVerifiedStatus
1VGG based5-class test accuracy66.13—Unverified
#ModelMetricClaimedVerifiedStatus
1MaSaC-ERC-ZF1-score (Weighted)51.17—Unverified
#ModelMetricClaimedVerifiedStatus
1BiHDMAccuracy40.34—Unverified
#ModelMetricClaimedVerifiedStatus
1w2v2-L-robust-12Concordance correlation coefficient (CCC)0.64—Unverified
#ModelMetricClaimedVerifiedStatus
14D-aNNAccuracy96.1—Unverified
#ModelMetricClaimedVerifiedStatus
1CNN1'"1—Unverified